Weakly supervised deep learning for covid-19 infection detection and classification from ct images
An outbreak of a novel coronavirus disease (ie, COVID-19) has been recorded in Wuhan,
China since late December 2019, which subsequently became pandemic around the world …
China since late December 2019, which subsequently became pandemic around the world …
An overview of deep learning methods for left ventricle segmentation
Cardiac health diseases are one of the key causes of death around the globe. The number
of heart patients has considerably increased during the pandemic. Therefore, it is crucial to …
of heart patients has considerably increased during the pandemic. Therefore, it is crucial to …
[HTML][HTML] Transmed: Transformers advance multi-modal medical image classification
Y Dai, Y Gao, F Liu - Diagnostics, 2021 - mdpi.com
Over the past decade, convolutional neural networks (CNN) have shown very competitive
performance in medical image analysis tasks, such as disease classification, tumor …
performance in medical image analysis tasks, such as disease classification, tumor …
[HTML][HTML] Deep learning in spatiotemporal cardiac imaging: A review of methodologies and clinical usability
KAL Hernandez, T Rienmüller, D Baumgartner… - Computers in Biology …, 2021 - Elsevier
The use of different cardiac imaging modalities such as MRI, CT or ultrasound enables the
visualization and interpretation of altered morphological structures and function of the heart …
visualization and interpretation of altered morphological structures and function of the heart …
Hybrid dilation and attention residual U-Net for medical image segmentation
Z Wang, Y Zou, PX Liu - Computers in biology and medicine, 2021 - Elsevier
Medical image segmentation is a typical task in medical image processing and critical
foundation in medical image analysis. U-Net is well-liked in medical image segmentation …
foundation in medical image analysis. U-Net is well-liked in medical image segmentation …
Multi-level semantic adaptation for few-shot segmentation on cardiac image sequences
Obtaining manual labels is time-consuming and labor-intensive on cardiac image
sequences. Few-shot segmentation can utilize limited labels to learn new tasks. However, it …
sequences. Few-shot segmentation can utilize limited labels to learn new tasks. However, it …
Iterative sparse and deep learning for accurate diagnosis of Alzheimer's disease
Deep learning techniques have been increasingly applied to the diagnosis of Alzheimer's
disease (AD) and the conversion from mild cognitive impairment (MCI) to AD. Despite their …
disease (AD) and the conversion from mild cognitive impairment (MCI) to AD. Despite their …
Echocardiography segmentation with enforced temporal consistency
Convolutional neural networks (CNN) have demonstrated their ability to segment 2D cardiac
ultrasound images. However, despite recent successes according to which the intra …
ultrasound images. However, despite recent successes according to which the intra …
Unsupervised inter-frame motion correction for whole-body dynamic PET using convolutional long short-term memory in a convolutional neural network
Subject motion in whole-body dynamic PET introduces inter-frame mismatch and seriously
impacts parametric imaging. Traditional non-rigid registration methods are generally …
impacts parametric imaging. Traditional non-rigid registration methods are generally …
PWD-3DNet: a deep learning-based fully-automated segmentation of multiple structures on temporal bone CT scans
S Nikan, K Van Osch, M Bartling… - … on Image Processing, 2020 - ieeexplore.ieee.org
The temporal bone is a part of the lateral skull surface that contains organs responsible for
hearing and balance. Mastering surgery of the temporal bone is challenging because of this …
hearing and balance. Mastering surgery of the temporal bone is challenging because of this …